Demonstrate full circle wide field imagingΒΆ
This include prediction of components, inversion, point source fitting. We will compare the output images with the input models, looking for closeness in flux and position.
%matplotlib inline
import os
import sys
sys.path.append(os.path.join('..', '..'))
from data_models.parameters import arl_path
results_dir = arl_path('test_results')
from matplotlib import pylab
pylab.rcParams['figure.figsize'] = (10.0, 10.0)
pylab.rcParams['image.cmap'] = 'rainbow'
from matplotlib import pyplot as plt
import numpy
from astropy.coordinates import SkyCoord
from astropy.time import Time
from astropy import units as u
from astropy.wcs.utils import pixel_to_skycoord
from data_models.polarisation import PolarisationFrame
from processing_components.image import image_raster_iter
from processing_components.visibility import create_visibility
from processing_components.visibility import sum_visibility
from processing_components.visibility import vis_timeslices, vis_wslices
from processing_components.simulation.configurations import create_named_configuration
from processing_components.skycomponent import create_skycomponent, find_skycomponents, \
find_nearest_skycomponent, insert_skycomponent
from processing_components.image import show_image, export_image_to_fits, qa_image, smooth_image
from processing_components.simulation.configurations import create_named_configuration
from processing_components.imaging import advise_wide_field, create_image_from_visibility, \
predict_skycomponent_visibility
from processing_components.griddata import create_awterm_convolutionfunction
from processing_components.griddata.convolution_functions import apply_bounding_box_convolutionfunction
# Use workflows for imaging
from wrappers.arlexecute.execution_support.arlexecute import arlexecute
from workflows.shared.imaging import imaging_contexts
from workflows.arlexecute.imaging.imaging_arlexecute import predict_list_arlexecute_workflow, \
invert_list_arlexecute_workflow
import logging
log = logging.getLogger()
log.setLevel(logging.DEBUG)
log.addHandler(logging.StreamHandler(sys.stdout))
Loaded backend module://ipykernel.pylab.backend_inline version unknown.
pylab.rcParams['figure.figsize'] = (12.0, 12.0)
pylab.rcParams['image.cmap'] = 'rainbow'
Construct the SKA1-LOW core configuration
lowcore = create_named_configuration('LOWBD2-CORE')
create_named_configuration: LOWBD2-CORE
(<Quantity -2565018.31203579 m>, <Quantity 5085711.90373391 m>, <Quantity -2861033.10788063 m>)
GeodeticLocation(lon=<Longitude 116.76444824 deg>, lat=<Latitude -26.82472208 deg>, height=<Quantity 300. m>)
create_configuration_from_file: 166 antennas/stations
Use Dask
arlexecute.set_client(use_dask=True)
Using selector: EpollSelector
Using selector: EpollSelector
/home/jenkins-slave/workspace/ce-library_feature-improved-docs/_build/lib/python3.5/site-packages/distributed/dashboard/core.py:72: UserWarning:
Port 8787 is already in use.
Perhaps you already have a cluster running?
Hosting the diagnostics dashboard on a random port instead.
warnings.warn("n" + msg)
We create the visibility. This just makes the uvw, time, antenna1, antenna2, weight columns in a table
times = numpy.array([-3.0, -2.0, -1.0, 0.0, 1.0, 2.0, 3.0]) * (numpy.pi / 12.0)
frequency = numpy.array([1e8])
channel_bandwidth = numpy.array([1e6])
reffrequency = numpy.max(frequency)
phasecentre = SkyCoord(ra=+15.0 * u.deg, dec=-45.0 * u.deg, frame='icrs', equinox='J2000')
vt = create_visibility(lowcore, times, frequency, channel_bandwidth=channel_bandwidth,
weight=1.0, phasecentre=phasecentre,
polarisation_frame=PolarisationFrame('stokesI'))
create_visibility: 95865 rows, 0.010 GB
create_visibility: flagged 0/95865 visibilities below elevation limit 0.261799 (rad)
advice = advise_wide_field(vt, wprojection_planes=1)
advise_wide_field: Maximum wavelength 2.998 (meters)
advise_wide_field: Minimum wavelength 2.998 (meters)
advise_wide_field: Maximum baseline 262.6 (wavelengths)
advise_wide_field: Maximum w 169.4 (wavelengths)
advise_wide_field: Station/dish diameter 35.0 (meters)
advise_wide_field: Primary beam 0.0857 (rad) 4.91 (deg) 1.77e+04 (asec)
advise_wide_field: Image field of view 0.514 (rad) 29.4 (deg) 1.06e+05 (asec)
advise_wide_field: Synthesized beam 0.00381 (rad) 0.218 (deg) 785 (asec)
advise_wide_field: Cellsize 0.00127 (rad) 0.0727 (deg) 262 (asec)
advice_wide_field: Npixels per side = 405
advice_wide_field: Npixels (power of 2) per side = 512
advice_wide_field: Npixels (power of 2, 3) per side = 512
advice_wide_field: Npixels (power of 2, 3, 4, 5) per side = 405
advice_wide_field: W sampling for full image = 0.2 (wavelengths)
advice_wide_field: W sampling for primary beam = 8.7 (wavelengths)
advice_wide_field: Time sampling for full image = 25.2 (s)
advice_wide_field: Time sampling for primary beam = 908.6 (s)
advice_wide_field: Frequency sampling for full image = 29212.6 (Hz)
advice_wide_field: Frequency sampling for primary beam = 1051653.8 (Hz)
advice_wide_field: Number of planes in w stack 39 (primary beam)
advice_wide_field: Number of planes in w projection 39 (primary beam)
advice_wide_field: W support = 6 (pixels) (primary beam)
Fill in the visibility with exact calculation of a number of point sources
vt.data['vis'] *= 0.0
npixel=256
model = create_image_from_visibility(vt, npixel=npixel, cellsize=0.001, nchan=1,
polarisation_frame=PolarisationFrame('stokesI'))
centre = model.wcs.wcs.crpix-1
spacing_pixels = npixel // 8
log.info('Spacing in pixels = %s' % spacing_pixels)
spacing = model.wcs.wcs.cdelt * spacing_pixels
locations = [-3.5, -2.5, -1.5, -0.5, 0.5, 1.5, 2.5, 3.5]
original_comps = []
# We calculate the source positions in pixels and then calculate the
# world coordinates to put in the skycomponent description
for iy in locations:
for ix in locations:
if ix >= iy:
p = int(round(centre[0] + ix * spacing_pixels * numpy.sign(model.wcs.wcs.cdelt[0]))), \
int(round(centre[1] + iy * spacing_pixels * numpy.sign(model.wcs.wcs.cdelt[1])))
sc = pixel_to_skycoord(p[0], p[1], model.wcs)
log.info("Component at (%f, %f) [0-rel] %s" % (p[0], p[1], str(sc)))
flux = numpy.array([[100.0 + 2.0 * ix + iy * 20.0]])
comp = create_skycomponent(flux=flux, frequency=frequency, direction=sc,
polarisation_frame=PolarisationFrame('stokesI'))
original_comps.append(comp)
insert_skycomponent(model, comp)
predict_skycomponent_visibility(vt, original_comps)
cmodel = smooth_image(model)
show_image(cmodel)
plt.title("Smoothed model image")
plt.show()
create_image_from_visibility: Parsing parameters to get definition of WCS
create_image_from_visibility: Defining single channel Image at <SkyCoord (ICRS): (ra, dec) in deg
(15., -45.)>, starting frequency 100000000.0 Hz, and bandwidth 999999.99999 Hz
create_image_from_visibility: uvmax = 262.634709 wavelengths
create_image_from_visibility: Critical cellsize = 0.001904 radians, 0.109079 degrees
create_image_from_visibility: Cellsize = 0.001 radians, 0.0572958 degrees
create_image_from_visibility: image shape is [1, 1, 256, 256]
Spacing in pixels = 32
Component at (240.000000, 16.000000) [0-rel] <SkyCoord (ICRS): (ra, dec) in deg
(4.74378292, -51.0209895)>
insert_skycomponent: Using insert method Nearest
Component at (208.000000, 16.000000) [0-rel] <SkyCoord (ICRS): (ra, dec) in deg
(7.66144755, -51.22149637)>
insert_skycomponent: Using insert method Nearest
Component at (176.000000, 16.000000) [0-rel] <SkyCoord (ICRS): (ra, dec) in deg
(10.5917361, -51.35530369)>
insert_skycomponent: Using insert method Nearest
Component at (144.000000, 16.000000) [0-rel] <SkyCoord (ICRS): (ra, dec) in deg
(13.52971731, -51.42224945)>
insert_skycomponent: Using insert method Nearest
Component at (112.000000, 16.000000) [0-rel] <SkyCoord (ICRS): (ra, dec) in deg
(16.47028269, -51.42224945)>
insert_skycomponent: Using insert method Nearest
Component at (80.000000, 16.000000) [0-rel] <SkyCoord (ICRS): (ra, dec) in deg
(19.4082639, -51.35530369)>
insert_skycomponent: Using insert method Nearest
Component at (48.000000, 16.000000) [0-rel] <SkyCoord (ICRS): (ra, dec) in deg
(22.33855245, -51.22149637)>
insert_skycomponent: Using insert method Nearest
Component at (16.000000, 16.000000) [0-rel] <SkyCoord (ICRS): (ra, dec) in deg
(25.25621708, -51.0209895)>
insert_skycomponent: Using insert method Nearest
Component at (208.000000, 48.000000) [0-rel] <SkyCoord (ICRS): (ra, dec) in deg
(7.94046978, -49.38802398)>
insert_skycomponent: Using insert method Nearest
Component at (176.000000, 48.000000) [0-rel] <SkyCoord (ICRS): (ra, dec) in deg
(10.76004686, -49.51635065)>
insert_skycomponent: Using insert method Nearest
Component at (144.000000, 48.000000) [0-rel] <SkyCoord (ICRS): (ra, dec) in deg
(13.58597172, -49.5805407)>
insert_skycomponent: Using insert method Nearest
Component at (112.000000, 48.000000) [0-rel] <SkyCoord (ICRS): (ra, dec) in deg
(16.41402828, -49.5805407)>
insert_skycomponent: Using insert method Nearest
Component at (80.000000, 48.000000) [0-rel] <SkyCoord (ICRS): (ra, dec) in deg
(19.23995314, -49.51635065)>
insert_skycomponent: Using insert method Nearest
Component at (48.000000, 48.000000) [0-rel] <SkyCoord (ICRS): (ra, dec) in deg
(22.05953022, -49.38802398)>
insert_skycomponent: Using insert method Nearest
Component at (16.000000, 48.000000) [0-rel] <SkyCoord (ICRS): (ra, dec) in deg
(24.86868384, -49.19566221)>
insert_skycomponent: Using insert method Nearest
Component at (176.000000, 80.000000) [0-rel] <SkyCoord (ICRS): (ra, dec) in deg
(10.91156133, -47.68176393)>
insert_skycomponent: Using insert method Nearest
Component at (144.000000, 80.000000) [0-rel] <SkyCoord (ICRS): (ra, dec) in deg
(13.63659973, -47.74353279)>
insert_skycomponent: Using insert method Nearest
Component at (112.000000, 80.000000) [0-rel] <SkyCoord (ICRS): (ra, dec) in deg
(16.36340027, -47.74353279)>
insert_skycomponent: Using insert method Nearest
Component at (80.000000, 80.000000) [0-rel] <SkyCoord (ICRS): (ra, dec) in deg
(19.08843867, -47.68176393)>
insert_skycomponent: Using insert method Nearest
Component at (48.000000, 80.000000) [0-rel] <SkyCoord (ICRS): (ra, dec) in deg
(21.80822877, -47.55825467)>
insert_skycomponent: Using insert method Nearest
Component at (16.000000, 80.000000) [0-rel] <SkyCoord (ICRS): (ra, dec) in deg
(24.51939763, -47.3730574)>
insert_skycomponent: Using insert method Nearest
Component at (144.000000, 112.000000) [0-rel] <SkyCoord (ICRS): (ra, dec) in deg
(13.68235336, -45.90931658)>
insert_skycomponent: Using insert method Nearest
Component at (112.000000, 112.000000) [0-rel] <SkyCoord (ICRS): (ra, dec) in deg
(16.31764664, -45.90931658)>
insert_skycomponent: Using insert method Nearest
Component at (80.000000, 112.000000) [0-rel] <SkyCoord (ICRS): (ra, dec) in deg
(18.95148298, -45.84967813)>
insert_skycomponent: Using insert method Nearest
Component at (48.000000, 112.000000) [0-rel] <SkyCoord (ICRS): (ra, dec) in deg
(21.58097973, -45.73040913)>
insert_skycomponent: Using insert method Nearest
Component at (16.000000, 112.000000) [0-rel] <SkyCoord (ICRS): (ra, dec) in deg
(24.20334715, -45.55152143)>
insert_skycomponent: Using insert method Nearest
Component at (112.000000, 144.000000) [0-rel] <SkyCoord (ICRS): (ra, dec) in deg
(16.27614945, -44.07600819)>
insert_skycomponent: Using insert method Nearest
Component at (80.000000, 144.000000) [0-rel] <SkyCoord (ICRS): (ra, dec) in deg
(18.82724608, -44.01824481)>
insert_skycomponent: Using insert method Nearest
Component at (48.000000, 144.000000) [0-rel] <SkyCoord (ICRS): (ra, dec) in deg
(21.37476195, -43.90270873)>
insert_skycomponent: Using insert method Nearest
Component at (16.000000, 144.000000) [0-rel] <SkyCoord (ICRS): (ra, dec) in deg
(23.91639614, -43.72937783)>
insert_skycomponent: Using insert method Nearest
Component at (80.000000, 176.000000) [0-rel] <SkyCoord (ICRS): (ra, dec) in deg
(18.71420256, -42.18561677)>
insert_skycomponent: Using insert method Nearest
Component at (48.000000, 176.000000) [0-rel] <SkyCoord (ICRS): (ra, dec) in deg
(21.18706763, -42.07336315)>
insert_skycomponent: Using insert method Nearest
Component at (16.000000, 176.000000) [0-rel] <SkyCoord (ICRS): (ra, dec) in deg
(23.65510465, -41.90491964)>
insert_skycomponent: Using insert method Nearest
Component at (48.000000, 208.000000) [0-rel] <SkyCoord (ICRS): (ra, dec) in deg
(21.01580068, -40.24055692)>
insert_skycomponent: Using insert method Nearest
Component at (16.000000, 208.000000) [0-rel] <SkyCoord (ICRS): (ra, dec) in deg
(23.41659285, -40.07639948)>
insert_skycomponent: Using insert method Nearest
Component at (16.000000, 240.000000) [0-rel] <SkyCoord (ICRS): (ra, dec) in deg
(23.19843619, -38.24201755)>
insert_skycomponent: Using insert method Nearest
locator: <matplotlib.colorbar._ColorbarAutoLocator object at 0x7f5db8541cf8>
Using auto colorbar locator on colorbar
locator: <matplotlib.colorbar._ColorbarAutoLocator object at 0x7f5db8541cf8>
Setting pcolormesh
update_title_pos
findfont: Matching :family=sans-serif:style=normal:variant=normal:weight=normal:stretch=normal:size=10.0 to DejaVu Sans ('/home/jenkins-slave/workspace/ce-library_feature-improved-docs/_build/lib/python3.5/site-packages/matplotlib/mpl-data/fonts/ttf/DejaVuSans.ttf') with score of 0.050000.
findfont: Matching :family=STIXGeneral:style=normal:variant=normal:weight=bold:stretch=normal:size=10.0 to STIXGeneral ('/home/jenkins-slave/workspace/ce-library_feature-improved-docs/_build/lib/python3.5/site-packages/matplotlib/mpl-data/fonts/ttf/STIXGeneralBol.ttf') with score of 0.000000.
findfont: Matching :family=STIXGeneral:style=normal:variant=normal:weight=normal:stretch=normal:size=10.0 to STIXGeneral ('/home/jenkins-slave/workspace/ce-library_feature-improved-docs/_build/lib/python3.5/site-packages/matplotlib/mpl-data/fonts/ttf/STIXGeneral.ttf') with score of 0.050000.
findfont: Matching :family=STIXSizeTwoSym:style=normal:variant=normal:weight=normal:stretch=normal:size=10.0 to STIXSizeTwoSym ('/home/jenkins-slave/workspace/ce-library_feature-improved-docs/_build/lib/python3.5/site-packages/matplotlib/mpl-data/fonts/ttf/STIXSizTwoSymReg.ttf') with score of 0.050000.
findfont: Matching :family=STIXSizeThreeSym:style=normal:variant=normal:weight=normal:stretch=normal:size=10.0 to STIXSizeThreeSym ('/home/jenkins-slave/workspace/ce-library_feature-improved-docs/_build/lib/python3.5/site-packages/matplotlib/mpl-data/fonts/ttf/STIXSizThreeSymReg.ttf') with score of 0.050000.
findfont: Matching :family=STIXSizeFourSym:style=normal:variant=normal:weight=normal:stretch=normal:size=10.0 to STIXSizeFourSym ('/home/jenkins-slave/workspace/ce-library_feature-improved-docs/_build/lib/python3.5/site-packages/matplotlib/mpl-data/fonts/ttf/STIXSizFourSymReg.ttf') with score of 0.050000.
findfont: Matching :family=STIXNonUnicode:style=normal:variant=normal:weight=bold:stretch=normal:size=10.0 to STIXNonUnicode ('/home/jenkins-slave/workspace/ce-library_feature-improved-docs/_build/lib/python3.5/site-packages/matplotlib/mpl-data/fonts/ttf/STIXNonUniBol.ttf') with score of 0.000000.
findfont: Matching :family=STIXSizeOneSym:style=normal:variant=normal:weight=normal:stretch=normal:size=10.0 to STIXSizeOneSym ('/home/jenkins-slave/workspace/ce-library_feature-improved-docs/_build/lib/python3.5/site-packages/matplotlib/mpl-data/fonts/ttf/STIXSizOneSymReg.ttf') with score of 0.050000.
findfont: Matching :family=STIXNonUnicode:style=italic:variant=normal:weight=normal:stretch=normal:size=10.0 to STIXNonUnicode ('/home/jenkins-slave/workspace/ce-library_feature-improved-docs/_build/lib/python3.5/site-packages/matplotlib/mpl-data/fonts/ttf/STIXNonUniIta.ttf') with score of 0.050000.
findfont: Matching :family=STIXNonUnicode:style=normal:variant=normal:weight=normal:stretch=normal:size=10.0 to STIXNonUnicode ('/home/jenkins-slave/workspace/ce-library_feature-improved-docs/_build/lib/python3.5/site-packages/matplotlib/mpl-data/fonts/ttf/STIXNonUni.ttf') with score of 0.050000.
findfont: Matching :family=STIXGeneral:style=italic:variant=normal:weight=normal:stretch=normal:size=10.0 to STIXGeneral ('/home/jenkins-slave/workspace/ce-library_feature-improved-docs/_build/lib/python3.5/site-packages/matplotlib/mpl-data/fonts/ttf/STIXGeneralItalic.ttf') with score of 0.050000.
findfont: Matching :family=STIXSizeFiveSym:style=normal:variant=normal:weight=normal:stretch=normal:size=10.0 to STIXSizeFiveSym ('/home/jenkins-slave/workspace/ce-library_feature-improved-docs/_build/lib/python3.5/site-packages/matplotlib/mpl-data/fonts/ttf/STIXSizFiveSymReg.ttf') with score of 0.050000.
findfont: Matching :family=cmtt10:style=normal:variant=normal:weight=normal:stretch=normal:size=10.0 to cmtt10 ('/home/jenkins-slave/workspace/ce-library_feature-improved-docs/_build/lib/python3.5/site-packages/matplotlib/mpl-data/fonts/ttf/cmtt10.ttf') with score of 0.050000.
findfont: Matching :family=cmb10:style=normal:variant=normal:weight=normal:stretch=normal:size=10.0 to cmb10 ('/home/jenkins-slave/workspace/ce-library_feature-improved-docs/_build/lib/python3.5/site-packages/matplotlib/mpl-data/fonts/ttf/cmb10.ttf') with score of 0.050000.
findfont: Matching :family=cmex10:style=normal:variant=normal:weight=normal:stretch=normal:size=10.0 to cmex10 ('/home/jenkins-slave/workspace/ce-library_feature-improved-docs/_build/lib/python3.5/site-packages/matplotlib/mpl-data/fonts/ttf/cmex10.ttf') with score of 0.050000.
findfont: Matching :family=cmsy10:style=normal:variant=normal:weight=normal:stretch=normal:size=10.0 to cmsy10 ('/home/jenkins-slave/workspace/ce-library_feature-improved-docs/_build/lib/python3.5/site-packages/matplotlib/mpl-data/fonts/ttf/cmsy10.ttf') with score of 0.050000.
findfont: Matching :family=cmss10:style=normal:variant=normal:weight=normal:stretch=normal:size=10.0 to cmss10 ('/home/jenkins-slave/workspace/ce-library_feature-improved-docs/_build/lib/python3.5/site-packages/matplotlib/mpl-data/fonts/ttf/cmss10.ttf') with score of 0.050000.
findfont: Matching :family=cmr10:style=normal:variant=normal:weight=normal:stretch=normal:size=10.0 to cmr10 ('/home/jenkins-slave/workspace/ce-library_feature-improved-docs/_build/lib/python3.5/site-packages/matplotlib/mpl-data/fonts/ttf/cmr10.ttf') with score of 0.050000.
findfont: Matching :family=cmmi10:style=normal:variant=normal:weight=normal:stretch=normal:size=10.0 to cmmi10 ('/home/jenkins-slave/workspace/ce-library_feature-improved-docs/_build/lib/python3.5/site-packages/matplotlib/mpl-data/fonts/ttf/cmmi10.ttf') with score of 0.050000.
findfont: Matching :family=DejaVu Sans:style=normal:variant=normal:weight=bold:stretch=normal:size=10.0 to DejaVu Sans ('/home/jenkins-slave/workspace/ce-library_feature-improved-docs/_build/lib/python3.5/site-packages/matplotlib/mpl-data/fonts/ttf/DejaVuSans-Bold.ttf') with score of 0.000000.
findfont: Matching :family=DejaVu Sans Mono:style=normal:variant=normal:weight=normal:stretch=normal:size=10.0 to DejaVu Sans Mono ('/home/jenkins-slave/workspace/ce-library_feature-improved-docs/_build/lib/python3.5/site-packages/matplotlib/mpl-data/fonts/ttf/DejaVuSansMono.ttf') with score of 0.050000.
findfont: Matching :family=DejaVu Sans:style=normal:variant=normal:weight=normal:stretch=normal:size=10.0 to DejaVu Sans ('/home/jenkins-slave/workspace/ce-library_feature-improved-docs/_build/lib/python3.5/site-packages/matplotlib/mpl-data/fonts/ttf/DejaVuSans.ttf') with score of 0.050000.
findfont: Matching :family=DejaVu Sans:style=italic:variant=normal:weight=normal:stretch=normal:size=10.0 to DejaVu Sans ('/home/jenkins-slave/workspace/ce-library_feature-improved-docs/_build/lib/python3.5/site-packages/matplotlib/mpl-data/fonts/ttf/DejaVuSans-Oblique.ttf') with score of 0.150000.
findfont: Matching :family=DejaVu Sans Display:style=normal:variant=normal:weight=normal:stretch=normal:size=10.0 to DejaVu Sans Display ('/home/jenkins-slave/workspace/ce-library_feature-improved-docs/_build/lib/python3.5/site-packages/matplotlib/mpl-data/fonts/ttf/DejaVuSansDisplay.ttf') with score of 0.050000.
findfont: Matching :family=sans-serif:style=normal:variant=normal:weight=normal:stretch=normal:size=12.0 to DejaVu Sans ('/home/jenkins-slave/workspace/ce-library_feature-improved-docs/_build/lib/python3.5/site-packages/matplotlib/mpl-data/fonts/ttf/DejaVuSans.ttf') with score of 0.050000.
update_title_pos
update_title_pos
update_title_pos
update_title_pos
update_title_pos
Check that the skycoordinate and image coordinate system are consistent by finding the point sources.
comps = find_skycomponents(cmodel, fwhm=1.0, threshold=10.0, npixels=5)
plt.clf()
for i in range(len(comps)):
ocomp, sep = find_nearest_skycomponent(comps[i].direction, original_comps)
plt.plot((comps[i].direction.ra.value - ocomp.direction.ra.value)/cmodel.wcs.wcs.cdelt[0],
(comps[i].direction.dec.value - ocomp.direction.dec.value)/cmodel.wcs.wcs.cdelt[1],
'.', color='r')
plt.xlabel('delta RA (pixels)')
plt.ylabel('delta DEC (pixels)')
plt.title("Recovered - Original position offsets")
plt.show()
find_skycomponents: Finding components in Image by segmentation
find_skycomponents: Identified 36 segments
update_title_pos
update_title_pos
update_title_pos
update_title_pos
Make the convolution function
wstep = 8.0
nw = int(1.1 * 800/wstep)
gcfcf = create_awterm_convolutionfunction(model, nw=110, wstep=8, oversampling=8,
support=60,
use_aaf=True)
cf=gcfcf[1]
print(cf.data.shape)
plt.clf()
plt.imshow(numpy.real(cf.data[0,0,0,0,0,:,:]))
plt.title(str(numpy.max(numpy.abs(cf.data[0,0,0,0,0,:,:]))))
plt.show()
cf_clipped = apply_bounding_box_convolutionfunction(cf, fractional_level=1e-3)
print(cf_clipped.data.shape)
gcfcf_clipped=(gcfcf[0], cf_clipped)
plt.clf()
plt.imshow(numpy.real(cf_clipped.data[0,0,0,0,0,:,:]))
plt.title(str(numpy.max(numpy.abs(cf_clipped.data[0,0,0,0,0,:,:]))))
plt.show()
create_w_term_image: For w = -440.0, field of view = 0.256000, Fresnel number = 7.21
create_w_term_image: For w = -432.0, field of view = 0.256000, Fresnel number = 7.08
create_w_term_image: For w = -424.0, field of view = 0.256000, Fresnel number = 6.95
create_w_term_image: For w = -416.0, field of view = 0.256000, Fresnel number = 6.82
create_w_term_image: For w = -408.0, field of view = 0.256000, Fresnel number = 6.68
create_w_term_image: For w = -400.0, field of view = 0.256000, Fresnel number = 6.55
create_w_term_image: For w = -392.0, field of view = 0.256000, Fresnel number = 6.42
create_w_term_image: For w = -384.0, field of view = 0.256000, Fresnel number = 6.29
create_w_term_image: For w = -376.0, field of view = 0.256000, Fresnel number = 6.16
create_w_term_image: For w = -368.0, field of view = 0.256000, Fresnel number = 6.03
create_w_term_image: For w = -360.0, field of view = 0.256000, Fresnel number = 5.90
create_w_term_image: For w = -352.0, field of view = 0.256000, Fresnel number = 5.77
create_w_term_image: For w = -344.0, field of view = 0.256000, Fresnel number = 5.64
create_w_term_image: For w = -336.0, field of view = 0.256000, Fresnel number = 5.51
create_w_term_image: For w = -328.0, field of view = 0.256000, Fresnel number = 5.37
create_w_term_image: For w = -320.0, field of view = 0.256000, Fresnel number = 5.24
create_w_term_image: For w = -312.0, field of view = 0.256000, Fresnel number = 5.11
create_w_term_image: For w = -304.0, field of view = 0.256000, Fresnel number = 4.98
create_w_term_image: For w = -296.0, field of view = 0.256000, Fresnel number = 4.85
create_w_term_image: For w = -288.0, field of view = 0.256000, Fresnel number = 4.72
create_w_term_image: For w = -280.0, field of view = 0.256000, Fresnel number = 4.59
create_w_term_image: For w = -272.0, field of view = 0.256000, Fresnel number = 4.46
create_w_term_image: For w = -264.0, field of view = 0.256000, Fresnel number = 4.33
create_w_term_image: For w = -256.0, field of view = 0.256000, Fresnel number = 4.19
create_w_term_image: For w = -248.0, field of view = 0.256000, Fresnel number = 4.06
create_w_term_image: For w = -240.0, field of view = 0.256000, Fresnel number = 3.93
create_w_term_image: For w = -232.0, field of view = 0.256000, Fresnel number = 3.80
create_w_term_image: For w = -224.0, field of view = 0.256000, Fresnel number = 3.67
create_w_term_image: For w = -216.0, field of view = 0.256000, Fresnel number = 3.54
create_w_term_image: For w = -208.0, field of view = 0.256000, Fresnel number = 3.41
create_w_term_image: For w = -200.0, field of view = 0.256000, Fresnel number = 3.28
create_w_term_image: For w = -192.0, field of view = 0.256000, Fresnel number = 3.15
create_w_term_image: For w = -184.0, field of view = 0.256000, Fresnel number = 3.01
create_w_term_image: For w = -176.0, field of view = 0.256000, Fresnel number = 2.88
create_w_term_image: For w = -168.0, field of view = 0.256000, Fresnel number = 2.75
create_w_term_image: For w = -160.0, field of view = 0.256000, Fresnel number = 2.62
create_w_term_image: For w = -152.0, field of view = 0.256000, Fresnel number = 2.49
create_w_term_image: For w = -144.0, field of view = 0.256000, Fresnel number = 2.36
create_w_term_image: For w = -136.0, field of view = 0.256000, Fresnel number = 2.23
create_w_term_image: For w = -128.0, field of view = 0.256000, Fresnel number = 2.10
create_w_term_image: For w = -120.0, field of view = 0.256000, Fresnel number = 1.97
create_w_term_image: For w = -112.0, field of view = 0.256000, Fresnel number = 1.84
create_w_term_image: For w = -104.0, field of view = 0.256000, Fresnel number = 1.70
create_w_term_image: For w = -96.0, field of view = 0.256000, Fresnel number = 1.57
create_w_term_image: For w = -88.0, field of view = 0.256000, Fresnel number = 1.44
create_w_term_image: For w = -80.0, field of view = 0.256000, Fresnel number = 1.31
create_w_term_image: For w = -72.0, field of view = 0.256000, Fresnel number = 1.18
create_w_term_image: For w = -64.0, field of view = 0.256000, Fresnel number = 1.05
create_w_term_image: For w = -56.0, field of view = 0.256000, Fresnel number = 0.92
create_w_term_image: For w = -48.0, field of view = 0.256000, Fresnel number = 0.79
create_w_term_image: For w = -40.0, field of view = 0.256000, Fresnel number = 0.66
create_w_term_image: For w = -32.0, field of view = 0.256000, Fresnel number = 0.52
create_w_term_image: For w = -24.0, field of view = 0.256000, Fresnel number = 0.39
create_w_term_image: For w = -16.0, field of view = 0.256000, Fresnel number = 0.26
create_w_term_image: For w = -8.0, field of view = 0.256000, Fresnel number = 0.13
create_w_term_image: For w = 0.0, field of view = 0.256000, Fresnel number = 0.00
create_w_term_image: For w = 8.0, field of view = 0.256000, Fresnel number = 0.13
create_w_term_image: For w = 16.0, field of view = 0.256000, Fresnel number = 0.26
create_w_term_image: For w = 24.0, field of view = 0.256000, Fresnel number = 0.39
create_w_term_image: For w = 32.0, field of view = 0.256000, Fresnel number = 0.52
create_w_term_image: For w = 40.0, field of view = 0.256000, Fresnel number = 0.66
create_w_term_image: For w = 48.0, field of view = 0.256000, Fresnel number = 0.79
create_w_term_image: For w = 56.0, field of view = 0.256000, Fresnel number = 0.92
create_w_term_image: For w = 64.0, field of view = 0.256000, Fresnel number = 1.05
create_w_term_image: For w = 72.0, field of view = 0.256000, Fresnel number = 1.18
create_w_term_image: For w = 80.0, field of view = 0.256000, Fresnel number = 1.31
create_w_term_image: For w = 88.0, field of view = 0.256000, Fresnel number = 1.44
create_w_term_image: For w = 96.0, field of view = 0.256000, Fresnel number = 1.57
create_w_term_image: For w = 104.0, field of view = 0.256000, Fresnel number = 1.70
create_w_term_image: For w = 112.0, field of view = 0.256000, Fresnel number = 1.84
create_w_term_image: For w = 120.0, field of view = 0.256000, Fresnel number = 1.97
create_w_term_image: For w = 128.0, field of view = 0.256000, Fresnel number = 2.10
create_w_term_image: For w = 136.0, field of view = 0.256000, Fresnel number = 2.23
create_w_term_image: For w = 144.0, field of view = 0.256000, Fresnel number = 2.36
create_w_term_image: For w = 152.0, field of view = 0.256000, Fresnel number = 2.49
create_w_term_image: For w = 160.0, field of view = 0.256000, Fresnel number = 2.62
create_w_term_image: For w = 168.0, field of view = 0.256000, Fresnel number = 2.75
create_w_term_image: For w = 176.0, field of view = 0.256000, Fresnel number = 2.88
create_w_term_image: For w = 184.0, field of view = 0.256000, Fresnel number = 3.01
create_w_term_image: For w = 192.0, field of view = 0.256000, Fresnel number = 3.15
create_w_term_image: For w = 200.0, field of view = 0.256000, Fresnel number = 3.28
create_w_term_image: For w = 208.0, field of view = 0.256000, Fresnel number = 3.41
create_w_term_image: For w = 216.0, field of view = 0.256000, Fresnel number = 3.54
create_w_term_image: For w = 224.0, field of view = 0.256000, Fresnel number = 3.67
create_w_term_image: For w = 232.0, field of view = 0.256000, Fresnel number = 3.80
create_w_term_image: For w = 240.0, field of view = 0.256000, Fresnel number = 3.93
create_w_term_image: For w = 248.0, field of view = 0.256000, Fresnel number = 4.06
create_w_term_image: For w = 256.0, field of view = 0.256000, Fresnel number = 4.19
create_w_term_image: For w = 264.0, field of view = 0.256000, Fresnel number = 4.33
create_w_term_image: For w = 272.0, field of view = 0.256000, Fresnel number = 4.46
create_w_term_image: For w = 280.0, field of view = 0.256000, Fresnel number = 4.59
create_w_term_image: For w = 288.0, field of view = 0.256000, Fresnel number = 4.72
create_w_term_image: For w = 296.0, field of view = 0.256000, Fresnel number = 4.85
create_w_term_image: For w = 304.0, field of view = 0.256000, Fresnel number = 4.98
create_w_term_image: For w = 312.0, field of view = 0.256000, Fresnel number = 5.11
create_w_term_image: For w = 320.0, field of view = 0.256000, Fresnel number = 5.24
create_w_term_image: For w = 328.0, field of view = 0.256000, Fresnel number = 5.37
create_w_term_image: For w = 336.0, field of view = 0.256000, Fresnel number = 5.51
create_w_term_image: For w = 344.0, field of view = 0.256000, Fresnel number = 5.64
create_w_term_image: For w = 352.0, field of view = 0.256000, Fresnel number = 5.77
create_w_term_image: For w = 360.0, field of view = 0.256000, Fresnel number = 5.90
create_w_term_image: For w = 368.0, field of view = 0.256000, Fresnel number = 6.03
create_w_term_image: For w = 376.0, field of view = 0.256000, Fresnel number = 6.16
create_w_term_image: For w = 384.0, field of view = 0.256000, Fresnel number = 6.29
create_w_term_image: For w = 392.0, field of view = 0.256000, Fresnel number = 6.42
create_w_term_image: For w = 400.0, field of view = 0.256000, Fresnel number = 6.55
create_w_term_image: For w = 408.0, field of view = 0.256000, Fresnel number = 6.68
create_w_term_image: For w = 416.0, field of view = 0.256000, Fresnel number = 6.82
create_w_term_image: For w = 424.0, field of view = 0.256000, Fresnel number = 6.95
create_w_term_image: For w = 432.0, field of view = 0.256000, Fresnel number = 7.08
(1, 1, 110, 8, 8, 60, 60)
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(1, 1, 110, 8, 8, 34, 34)
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Predict the visibility using the different approaches.
contexts = imaging_contexts().keys()
print(contexts)
dict_keys(['facets_timeslice', 'facets_wstack', 'wprojection', 'facets', '2d', 'wstack', 'wsnapshots', 'timeslice'])
print(gcfcf_clipped[1])
Convolution function:
Shape: (1, 1, 110, 8, 8, 34, 34)
Grid WCS: WCS Transformation
This transformation has 7 pixel and 7 world dimensions
Array shape (Numpy order): None
Pixel Dim Data size Bounds
0 None None
1 None None
2 None None
3 None None
4 None None
5 None None
6 None None
World Dim Physical Type Units
0 None unknown
1 None unknown
2 None unknown
3 None unknown
4 None unknown
5 None unknown
6 em.freq Hz
Correlation between pixel and world axes:
Pixel Dim
World Dim 0 1 2 3 4 5 6
0 yes no no no no no no
1 no yes no no no no no
2 no no yes no no no no
3 no no no yes no no no
4 no no no no yes no no
5 no no no no no yes no
6 no no no no no no yes
Projection WCS: WCS Transformation
This transformation has 4 pixel and 4 world dimensions
Array shape (Numpy order): None
Pixel Dim Data size Bounds
0 None None
1 None None
2 None None
3 None None
World Dim Physical Type Units
0 pos.eq.ra deg
1 pos.eq.dec deg
2 None unknown
3 em.freq Hz
Correlation between pixel and world axes:
Pixel Dim
World Dim 0 1 2 3
0 yes yes no no
1 yes yes no no
2 no no yes no
3 no no no yes
Polarisation frame: stokesI
contexts = ['2d', 'facets', 'timeslice', 'wstack', 'wprojection']
for context in contexts:
print('Processing context %s' % context)
vtpredict_list =[create_visibility(lowcore, times, frequency, channel_bandwidth=channel_bandwidth,
weight=1.0, phasecentre=phasecentre, polarisation_frame=PolarisationFrame('stokesI'))]
model_list = [model]
vtpredict_list = arlexecute.compute(vtpredict_list, sync=True)
vtpredict_list = arlexecute.scatter(vtpredict_list)
if context == 'wprojection':
future = predict_list_arlexecute_workflow(vtpredict_list, model_list, context='2d', gcfcf=[gcfcf_clipped])
elif context == 'facets':
future = predict_list_arlexecute_workflow(vtpredict_list, model_list, context=context, facets=8)
elif context == 'timeslice':
future = predict_list_arlexecute_workflow(vtpredict_list, model_list, context=context, vis_slices=vis_timeslices(
vtpredict, 'auto'))
elif context == 'wstack':
future = predict_list_arlexecute_workflow(vtpredict_list, model_list, context=context, vis_slices=31)
else:
future = predict_list_arlexecute_workflow(vtpredict_list, model_list, context=context)
vtpredict_list = arlexecute.compute(future, sync=True)
vtpredict = vtpredict_list[0]
uvdist = numpy.sqrt(vt.data['uvw'][:, 0] ** 2 + vt.data['uvw'][:, 1] ** 2)
plt.clf()
plt.plot(uvdist, numpy.abs(vt.data['vis'][:]), '.', color='r', label="DFT")
plt.plot(uvdist, numpy.abs(vtpredict.data['vis'][:]), '.', color='b', label=context)
plt.plot(uvdist, numpy.abs(vtpredict.data['vis'][:] - vt.data['vis'][:]), '.', color='g', label="Residual")
plt.xlabel('uvdist')
plt.ylabel('Amp Visibility')
plt.legend()
plt.show()
Processing context 2d
create_visibility: 95865 rows, 0.010 GB
create_visibility: flagged 0/95865 visibilities below elevation limit 0.261799 (rad)
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Processing context facets
create_visibility: 95865 rows, 0.010 GB
create_visibility: flagged 0/95865 visibilities below elevation limit 0.261799 (rad)
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Processing context timeslice
create_visibility: 95865 rows, 0.010 GB
create_visibility: flagged 0/95865 visibilities below elevation limit 0.261799 (rad)
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Processing context wstack
create_visibility: 95865 rows, 0.010 GB
create_visibility: flagged 0/95865 visibilities below elevation limit 0.261799 (rad)
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Processing context wprojection
create_visibility: 95865 rows, 0.010 GB
create_visibility: flagged 0/95865 visibilities below elevation limit 0.261799 (rad)
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Make the image using the different approaches. We will evaluate the results using a number of plots:
The error in fitted versus the radius. The ideal result is a straightline fitted: flux = DFT flux
The offset in RA versus the offset in DEC. The ideal result is a cluster around 0 pixels.
The sampling in w is set to provide 2% decorrelation at the half power point of the primary beam.
contexts = ['2d', 'facets', 'timeslice', 'wstack', 'wprojection']
for context in contexts:
targetimage_list = [create_image_from_visibility(vt, npixel=npixel, cellsize=0.001, nchan=1,
polarisation_frame=PolarisationFrame('stokesI'))]
vt_list = [vt]
print('Processing context %s' % context)
if context == 'wprojection':
future = invert_list_arlexecute_workflow(vt_list, targetimage_list, context='2d', gcfcf=[gcfcf_clipped])
elif context == 'facets':
future = invert_list_arlexecute_workflow(vt_list, targetimage_list, context=context, facets=8)
elif context == 'timeslice':
future = invert_list_arlexecute_workflow(vt_list, targetimage_list, context=context, vis_slices=vis_timeslices(vt, 'auto'))
elif context == 'wstack':
future = invert_list_arlexecute_workflow(vt_list, targetimage_list, context=context, vis_slices=31)
else:
future = invert_list_arlexecute_workflow(vt_list, targetimage_list, context=context)
result = arlexecute.compute(future, sync=True)
targetimage = result[0][0]
show_image(targetimage)
plt.title(context)
plt.show()
print("Dirty Image %s" % qa_image(targetimage, context="imaging-fits notebook, using processor %s" % context))
export_image_to_fits(targetimage, '%s/imaging-fits_dirty_%s.fits' % (results_dir, context))
comps = find_skycomponents(targetimage, fwhm=1.0, threshold=10.0, npixels=5)
plt.clf()
for comp in comps:
distance = comp.direction.separation(model.phasecentre)
dft_flux = sum_visibility(vt, comp.direction)[0]
err = (comp.flux[0, 0] - dft_flux) / dft_flux
plt.plot(distance, err, '.', color='r')
plt.ylabel('Fractional error of image vs DFT')
plt.xlabel('Distance from phasecentre (deg)')
plt.title(
"Fractional error in %s recovered flux vs distance from phasecentre" %
context)
plt.show()
checkpositions = True
if checkpositions:
plt.clf()
for i in range(len(comps)):
ocomp, sep = find_nearest_skycomponent(comps[i].direction, original_comps)
plt.plot(
(comps[i].direction.ra.value - ocomp.direction.ra.value) /
targetimage.wcs.wcs.cdelt[0],
(comps[i].direction.dec.value - ocomp.direction.dec.value) /
targetimage.wcs.wcs.cdelt[1],
'.',
color='r')
plt.xlabel('delta RA (pixels)')
plt.ylabel('delta DEC (pixels)')
plt.title("%s: Position offsets" % context)
plt.show()
create_image_from_visibility: Parsing parameters to get definition of WCS
create_image_from_visibility: Defining single channel Image at <SkyCoord (ICRS): (ra, dec) in deg
(15., -45.)>, starting frequency 100000000.0 Hz, and bandwidth 999999.99999 Hz
create_image_from_visibility: uvmax = 262.634709 wavelengths
create_image_from_visibility: Critical cellsize = 0.001904 radians, 0.109079 degrees
create_image_from_visibility: Cellsize = 0.001 radians, 0.0572958 degrees
create_image_from_visibility: image shape is [1, 1, 256, 256]
Processing context 2d
locator: <matplotlib.colorbar._ColorbarAutoLocator object at 0x7f5d87dc4518>
Using auto colorbar locator on colorbar
locator: <matplotlib.colorbar._ColorbarAutoLocator object at 0x7f5d87dc4518>
Setting pcolormesh
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Dirty Image Quality assessment:
Origin: qa_image
Context: imaging-fits notebook, using processor 2d
Data:
medianabsdevmedian: '1.044301320108919'
rms: '4.886194600489749'
maxabs: '108.20526767700511'
min: '-7.218746872758734'
medianabs: '1.1714737858502642'
sum: '1981.2706451419556'
median: '-0.6052575706650143'
shape: '(1, 1, 256, 256)'
max: '108.20526767700511'
find_skycomponents: Finding components in Image by segmentation
find_skycomponents: Identified 28 segments
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create_image_from_visibility: Parsing parameters to get definition of WCS
create_image_from_visibility: Defining single channel Image at <SkyCoord (ICRS): (ra, dec) in deg
(15., -45.)>, starting frequency 100000000.0 Hz, and bandwidth 999999.99999 Hz
create_image_from_visibility: uvmax = 262.634709 wavelengths
create_image_from_visibility: Critical cellsize = 0.001904 radians, 0.109079 degrees
create_image_from_visibility: Cellsize = 0.001 radians, 0.0572958 degrees
create_image_from_visibility: image shape is [1, 1, 256, 256]
Processing context facets
locator: <matplotlib.colorbar._ColorbarAutoLocator object at 0x7f5d1c5326a0>
Using auto colorbar locator on colorbar
locator: <matplotlib.colorbar._ColorbarAutoLocator object at 0x7f5d1c5326a0>
Setting pcolormesh
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Dirty Image Quality assessment:
Origin: qa_image
Context: imaging-fits notebook, using processor facets
Data:
medianabsdevmedian: '1.0709711879879817'
rms: '6.62858474773288'
maxabs: '176.52818347943742'
min: '-14.780837365532502'
medianabs: '1.1844165351973075'
sum: '-1283.579563263747'
median: '-0.6876741395199579'
shape: '(1, 1, 256, 256)'
max: '176.52818347943742'
find_skycomponents: Finding components in Image by segmentation
find_skycomponents: Identified 36 segments
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create_image_from_visibility: Parsing parameters to get definition of WCS
create_image_from_visibility: Defining single channel Image at <SkyCoord (ICRS): (ra, dec) in deg
(15., -45.)>, starting frequency 100000000.0 Hz, and bandwidth 999999.99999 Hz
create_image_from_visibility: uvmax = 262.634709 wavelengths
create_image_from_visibility: Critical cellsize = 0.001904 radians, 0.109079 degrees
create_image_from_visibility: Cellsize = 0.001 radians, 0.0572958 degrees
create_image_from_visibility: image shape is [1, 1, 256, 256]
Processing context timeslice
locator: <matplotlib.colorbar._ColorbarAutoLocator object at 0x7f5db833dac8>
Using auto colorbar locator on colorbar
locator: <matplotlib.colorbar._ColorbarAutoLocator object at 0x7f5db833dac8>
Setting pcolormesh
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Dirty Image Quality assessment:
Origin: qa_image
Context: imaging-fits notebook, using processor timeslice
Data:
medianabsdevmedian: '1.005982389762876'
rms: '6.478730515928253'
maxabs: '172.81835860085235'
min: '-5.869581605851597'
medianabs: '1.1232164891451046'
sum: '3375.349471252534'
median: '-0.6422214152170783'
shape: '(1, 1, 256, 256)'
max: '172.81835860085235'
find_skycomponents: Finding components in Image by segmentation
find_skycomponents: Identified 36 segments
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create_image_from_visibility: Parsing parameters to get definition of WCS
create_image_from_visibility: Defining single channel Image at <SkyCoord (ICRS): (ra, dec) in deg
(15., -45.)>, starting frequency 100000000.0 Hz, and bandwidth 999999.99999 Hz
create_image_from_visibility: uvmax = 262.634709 wavelengths
create_image_from_visibility: Critical cellsize = 0.001904 radians, 0.109079 degrees
create_image_from_visibility: Cellsize = 0.001 radians, 0.0572958 degrees
create_image_from_visibility: image shape is [1, 1, 256, 256]
Processing context wstack
locator: <matplotlib.colorbar._ColorbarAutoLocator object at 0x7f5d17fc62b0>
Using auto colorbar locator on colorbar
locator: <matplotlib.colorbar._ColorbarAutoLocator object at 0x7f5d17fc62b0>
Setting pcolormesh
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Dirty Image Quality assessment:
Origin: qa_image
Context: imaging-fits notebook, using processor wstack
Data:
medianabsdevmedian: '18760.15186293877'
rms: '38814.80085722214'
maxabs: '610905.3659107479'
min: '-180837.48427518026'
medianabs: '18744.271913418095'
sum: '2155584.532164461'
median: '-508.61577714840485'
shape: '(1, 1, 256, 256)'
max: '610905.3659107479'
find_skycomponents: Finding components in Image by segmentation
find_skycomponents: Identified 100 segments
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create_image_from_visibility: Parsing parameters to get definition of WCS
create_image_from_visibility: Defining single channel Image at <SkyCoord (ICRS): (ra, dec) in deg
(15., -45.)>, starting frequency 100000000.0 Hz, and bandwidth 999999.99999 Hz
create_image_from_visibility: uvmax = 262.634709 wavelengths
create_image_from_visibility: Critical cellsize = 0.001904 radians, 0.109079 degrees
create_image_from_visibility: Cellsize = 0.001 radians, 0.0572958 degrees
create_image_from_visibility: image shape is [1, 1, 256, 256]
Processing context wprojection
locator: <matplotlib.colorbar._ColorbarAutoLocator object at 0x7f5d1c53ec88>
Using auto colorbar locator on colorbar
locator: <matplotlib.colorbar._ColorbarAutoLocator object at 0x7f5d1c53ec88>
Setting pcolormesh
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Dirty Image Quality assessment:
Origin: qa_image
Context: imaging-fits notebook, using processor wprojection
Data:
medianabsdevmedian: '1.0134461773288361'
rms: '6.464558076010437'
maxabs: '169.9395857007542'
min: '-5.924830369976826'
medianabs: '1.1314427316902298'
sum: '2837.4068390269294'
median: '-0.6544929780850646'
shape: '(1, 1, 256, 256)'
max: '169.9395857007542'
find_skycomponents: Finding components in Image by segmentation
find_skycomponents: Identified 36 segments
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